390 professor-computer "https:" "https:" "https:" "https:" "https:" "Dr" "St" "St" positions in Switzerland
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you will have the opportunity to contribute to teaching activities within the group. The position is funded for 4 years, at 100% employment. The project will be supervised by Dr. Céline Labouesse
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, Biostatistics, Software Engineering, Systems Operations, and Screening & Lab Automation. Embedded in this multi-disciplinary environment, the Clinical Bioinformatics group translates computational biology into
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institutions worldwide. Further information about Professorship for International Relations and Data Science can be found on our website. Questions regarding the position should be directed to Professor Dr
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100%, Zurich, fixed-term The Clinical Genomics team led by Dr. André Kahles at the Biomedical Informatics Lab (BMI Lab), headed by Prof. Gunnar Rätsch, at ETH Zurich, is seeking a highly motivated
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100%, Basel, fixed-term A World-Class Research Environment at the Nexus of Biology, Engineering, and Physical Sciences The Biotechnology and Bioengineering group led by Prof. Dr. Martin Fussenegger
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100%, Basel, fixed-term A World-Class Research Environment at the Nexus of Biology, Engineering, and Physical Sciences The Biotechnology and Bioengineering group led by Prof. Dr. Martin Fussenegger
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(cover letter, transcripts, CV, references) with reference “Research Intern - Robotics” to our HR department (hr@inspire.ch). Curious? So are we. For technical questions, please contact Dr. Efe Balta
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evaluate prototypes together with industrial partners Profile Required experience CH/EU/EFTA citizenship or valid Swiss work permit PhD in Engineering, Computer Science, Robotics, or related field Strong
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datasets The position is limited to two years. Profile University degree (MSc or PhD) in data science, computer science, physics or a related field Experience in training and validating large-scale deep
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years. Profile University degree (MSc or PhD) in data science, computer science, physics or a related field Experience in training and validating large-scale deep-learning models on distributed systems